Multi-graph-view learning for graph classification

Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

28 Citations (Scopus)

Abstract

Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to have useful information from different channels/views to describe objects, which naturally results in a new representation with multiple graphs generated from different feature views being used to describe one object. In this paper, we formulate a new Multi-Graph-View learning task for graph classification, where each object to be classified contains graphs from multiple graph-views. This problem setting is essentially different from traditional single-graph-view graph classification, where graphs are from one single feature view. To solve the problem, we propose a Cross Graph-View Sub graph Feature based Learning (gCGVFL) algorithm that explores an optimal set of sub graphs, across multiple graph-views, as features to represent graphs. Specifically, we derive an evaluation criterion to estimate the discriminative power and the redundancy of sub graph features across all views, and assign proper weight values to each view to indicate its importance for graph classification. The iterative cross graph-view sub graph scoring and graph-view weight updating form a closed loop to find optimal sub graphs to represent graphs for multi-graph-view learning. Experiments and comparisons on real-world tasks demonstrate the algorithm's performance.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Data Mining - Proceedings
EditorsRavi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
Place of PublicationLos Alamitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages590-599
Number of pages10
ISBN (Electronic)9781479943029
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: 14 Dec 201417 Dec 2014

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference14th IEEE International Conference on Data Mining, ICDM 2014
CountryChina
CityShenzhen
Period14/12/1417/12/14

Keywords

  • Feature Selection
  • Graph Classification
  • Multi-Graph-View
  • Subgraph Mining

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  • Cite this

    Wu, J., Hong, Z., Pan, S., Zhu, X., Cai, Z., & Zhang, C. (2014). Multi-graph-view learning for graph classification. In R. Kumar, H. Toivonen, J. Pei, J. Z. Huang, & X. Wu (Eds.), 2014 IEEE International Conference on Data Mining - Proceedings (pp. 590-599). (Proceedings - IEEE International Conference on Data Mining, ICDM). Los Alamitos, CA: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICDM.2014.97